Tools Used : SQL,Excel
- Business Problem: To conduct a comprehensive analysis of Blinkit's sales performance, customer satisfaction, and inventory distribution to identify key insights and opportunities for optimization using various KPIs and visualizations in Power Bl. They need a robust and scalable data analytics solution to handle the amount of data and uncover valuable patterns and trends.
SELECT CONCAT(CAST(sum(sales)/1000000 as DECIMAL(10,2)),'M') as Total_Sales_Millions
from blinkit_grocery_data;Result:
Insight: The total revenue generated from all items sold amounts to a multi-million figure, indicating strong sales performance across the grocery catalog. This metric sets the foundation for evaluating overall business health and revenue trends.
SELECT CONCAT(CAST(AVG(sales) as DECIMAL(10,0)),'M') as Avg_Sales
from blinkit_grocery_data;Result:
Insight: This output shows the average sales value per item sold, offering a view into individual product performance. It helps assess whether revenue is driven by high-value products or a large volume of low-value items.
SELECT COUNT(*) AS Total_No_Items from blinkit_grocery_data;Result:
Insight: This query returns the count of all individual product entries, reflecting the scale and diversity of Blinkit's product offerings. A large item count suggests a wide assortment and potentially a broader market reach.
SELECT CAST(AVG(`Rating`) as DECIMAL(10,2)) as Avg_Rating
from blinkit_grocery_data;Result:
Insight: This metric provides the mean customer satisfaction score across all products. A high average rating indicates strong customer satisfaction and product quality, whereas a lower one could highlight the need for improvements.
Objective: Analyze the impact of fat content on total sales.
SELECT `Item Fat Content`,
CONCAT(CAST(sum(sales)/1000 as DECIMAL(10,2)),'k') as Total_Sales_Thousands,
CONCAT(CAST(AVG(sales) as DECIMAL(10,0)),'M') as Avg_Sales,
COUNT(*) AS Total_No_Items,
CAST(AVG(`Rating`) as DECIMAL(10,2)) as Avg_Rating
from blinkit_grocery_data
GROUP BY `Item Fat Content`
ORDER BY Total_Sales_Thousands DESC;Result:
Insight: This output segments sales based on fat content (e.g., Low Fat, Regular, etc.), revealing consumer preferences. Higher sales in certain fat categories can inform product stocking and health-based marketing strategies.
Objective: Identify the performance of different item types in terms of total sales.
SELECT `Item Type`,
CONCAT(CAST(sum(sales)/1000 as DECIMAL(10,2)),'k') as Total_Sales,
CONCAT(CAST(AVG(sales) as DECIMAL(10,0)),'M') as Avg_Sales,
COUNT(*) AS Total_No_Items,
CAST(AVG(`Rating`) as DECIMAL(10,2)) as Avg_Rating
from blinkit_grocery_data
GROUP BY `Item Type`
ORDER BY Total_Sales;Result:
Insight: By analyzing performance across product types (e.g., Dairy, Snacks, Beverages), this result pinpoints high-revenue categories. The business can use this to identify top-selling segments and plan inventory or promotions accordingly.
Objective: Compare total sales across different outlets segmented by fat content.
SELECT `Item Fat Content`,`Outlet Location Type`,
CONCAT(CAST(sum(sales)/1000 as DECIMAL(10,2)),'k') as Total_Sales,
CONCAT(CAST(AVG(sales) as DECIMAL(10,0)),'M') as Avg_Sales,
COUNT(*) AS Total_No_Items,
CAST(AVG(`Rating`) as DECIMAL(10,2)) as Avg_Rating
from blinkit_grocery_data
GROUP BY `Item Fat Content`,`Outlet Location Type`
ORDER BY Total_Sales;Result:
Insight: This analysis combines fat content with outlet location types to identify regional or store-type preferences. It uncovers consumption patterns and helps Blinkit align store-specific inventory with local customer demand.
Objective: Evaluate how the age or type of outlet establishment influences total sales.
SELECT `Outlet Establishment Year`,
CONCAT(CAST(SUM(`Sales`)/1000 as DECIMAL(10,2)),'k') as Total_Sales,
CONCAT(CAST(AVG(`Sales`) as DECIMAL(10,0)),'M') as Avg_Sales,
COUNT(*) as No_of_Items
from blinkit_grocery_data
GROUP BY `Outlet Establishment Year`
ORDER BY Total_Sales;Result:
Insight: This shows how much each outlet, based on its establishment year, contributes to total sales. Newer or older outlets with higher sales can guide decisions on expansion, renovation, or marketing efforts.
Objective: Analyze the correlation between outlet size and total sales.
SELECT `Outlet Size`,
CAST(SUM(`Sales`) as DECIMAL(10,2))as Total_Sales,
CAST((SUM(`Sales`) * 100.0 / SUM(SUM(`Sales`)) OVER()) as DECIMAL(10,2)) as Sales_Percentage
FROM blinkit_grocery_data
GROUP BY `Outlet Size`;Result:
Insight: This output provides a percentage breakdown of total sales by outlet size (Small, Medium, High). It highlights which store sizes contribute most to revenue and can help in optimizing store layouts or investments.
Objective: Assess the geographic distribution of sales across different locations.
SELECT `Outlet Location Type`,
CAST(SUM(`Sales`) as DECIMAL(10,2)) as Total_Sales,
CAST((SUM(`Sales`) * 100.0 / SUM(SUM(`Sales`)) OVER()) as DECIMAL(10,2)) as Sales_Percentage
FROM blinkit_grocery_data
GROUP BY `Outlet Location Type`;Result:
Insight: This shows the distribution of sales across different geographic locations (Urban, Semi-Urban, etc.), offering insights into regional performance. It helps target marketing and operations based on location-based profitability.
Objective: Provide a comprehensive view of all key metrics (Total Sales, Average Sales, Number of Items, Average Rating) broken down by different outlet types.
SELECT `Outlet Type`,
CONCAT(CAST(SUM(`Sales`)/1000 as DECIMAL(10,2)),'k') as Total_Sales,
CONCAT(CAST(AVG(`Sales`) as DECIMAL(10,0)),'M') as Avg_Sales,
COUNT(*) as No_of_Items,
CAST(AVG(`Rating`) as DECIMAL(10,2)) as Avg_Rating
from blinkit_grocery_data
GROUP BY `Outlet Type`
ORDER BY Total_Sales DESC;Result:
Insight: This comprehensive summary breaks down total sales, average sales, item count, and ratings by outlet type (e.g., Grocery Store, Supermarket). It provides a comparative performance view across formats, guiding strategic decisions on outlet expansion or improvement.